Genetic diversity analysis of East African sorghum (Sorghum bicolor [L.] Moench) germplasm collections for agronomic and nutritional quality traits

Breeding for climate-resilient, high-yielding, and nutrient-rich sorghum cultivars is essential for sustainable food systems and enhanced livelihoods in sub-Saharan Africa. Therefore, this study aimed to determine the genetic diversity among East African sorghum germplasm collections through agronomic and nutritional quality traits to select promising lines for direct production or breeding. A collection of 348 sorghum germplasm was field evaluated at two locations in Uganda using an augmented design, and grain iron (Fe) and zinc (Zn) contents were profiled. Data were collected on 20 sorghum agro-morphological traits and Fe and Zn compositions. A significant (P ≤ 0.05) variation was detected amongst the test genotypes for all the assessed traits, suggesting the presence of sufficient genetic diversity for selection. High heritability (H2 > 0.60) and genetic advance as percent of the mean (GA >20%) were computed for grain yield, Zn content, and selected agronomic traits, ensuring genetic gains through selection. A significant positive correlation was recorded between Fe and Zn concentrations (r = 0.32, P < 0.001), allowing simultaneous selection for the two nutrient compositions. Cluster analysis based on phenotypic traits resolved the test sorghum genotypes into four distinct genetic groups. Six genotypes with superior agronomic traits and high Fe and Zn contents were identified for production or potential parents for quality breeding. Overall, the current study found considerable genetic variation among East African sorghum germplasm collections for strategic conservation and breeding in Uganda or similar agro-ecologies.


Introduction
Sorghum [Sorghum bicolor (L.) Moench, 2n ¼ 2x ¼ 20] is the fifth most important cereal crop produced after rice, wheat, maize, and barley globally (FAOSTAT, 2021). Sorghum is a vital food staple and an essential source of human nutrition for millions of people in developing countries (Kumar et al., 2013b). It thrives under low soil moisture and poor soil fertility conditions, where other major cereal crops would fail. Sorghum is relatively tolerant to drought and heat stress making it a crop of choice in marginal agro-ecologies in sub-Saharan Africa (SSA).
Sorghum grain has appreciable levels of minerals, starch, gluten-free protein, and crude fiber (Kumar et al., 2015). The grain consists of about 65% carbohydrates and 15% total protein on a dry weight basis. The protein and starch in sorghum products have slow digestibility compared with other cereals. Hence sorghum products are ideal for people with gluten intolerance or diabetes.
Sorghum is an essential source of iron (Fe) and zinc (Zn) and possesses diverse mineral nutrients than rice and wheat (Chan et al., 2007). Some accessions possess higher Fe content of >60 mg kg À1 and Zn > 32 mg kg À1 . Biofortified sorghum provides a substantial amount of Zn (13, 000 to 18,000 mg/100g) and Fe (10,000 mg/100g) for children from 4 to 6 years old. Some 300g grain of biofortified sorghum can provide 39,000 to 54,000 mg Zn and 30,000 mg Fe to lactating women (Andiku et al., 2021). Iron supply is vital for producing new red blood cells, muscle growth, and brain development, while Zn is critical for embryo development, fetal growth, and milk expression in lactating mothers. A preliminary report by USAID (2021) indicated that a larger proportion of children below five years of age are stunted or malnourished in Uganda. Also, the report pointed out a higher prevalence of anaemia among children of 6-59 months of age and women of reproductive age due to micro-nutrient deficiency.
After maize and rice, sorghum is the third most important cereal crop in Uganda. It is cultivated across 470,083 ha accounting for 400,000 tons of grain production (FAOSTAT, 2021). Sorghum is cultivated for food, brewing, and feed in SSA and Asia (Andiku et al., 2021). It is the primary source of dietary calories, particularly for pregnant and lactating women and pre-school children in Uganda. Most farmers in Uganda grow unimproved sorghum varieties deficient in micronutrients such as Zn and Fe and with low yield potential. Furthermore, a lack of improved varieties and yield loss due to biotic and abiotic constraints are the major constraints to sorghum production in Uganda (Andiku et al., 2021). Therefore, there is a need to develop nutrient-dense, high-yielding sorghum varieties with farmer-preferred traits in the country.
Sorghum's nutritional quality has improved through conventional plant breeding, biotechnology, and crop management practices (Kumar et al., 2019). Biofortification through conventional breeding is cost-effective, and the new varieties and derived products are relatively widely adopted. The International Crops Research Institute for Semi and Arid Tropics (ICRISAT)/India has recently developed and released biofortified sorghum varieties using the conventional breeding method. The released biofortified sorghum varieties expressed higher grain Zn and Fe concentrations varying from 50 to 60% than the standard commercial cultivars (Janila et al., 2018;Kumar Ashok et al., 2018). In 2018, ICRI-SAT/Nigeria released the first two biofortified sorghum varieties in Africa. The released varieties had three times higher Fe concentrations and better grain yield (2.4-2.8 t ha À1 ) than the traditional sorghum cultivated in the region (Reddy and Reddy, 2019). The sorghum genetic diversity present in East Africa has not been explored to select unique genetic stocks for cultivar development with enhanced nutrition content and farmer-preferred attributes.
Phenotypic, genotypic, and biochemical markers have been used to assess the genetic diversity of sorghum. Phenotypic or agromorphological traits are relatively simple and cost-effective to measure and valuable for ideotype breeding. However, phenotypic selection for agronomic and nutritional traits is subject to genotype, environment, and genotype Â environment interaction effects. Therefore, a combination of phenotypic, genetic, and biochemical markers and multi-environmental evaluations is required for precision genotype selection and accelerated breeding. Various biochemical markers such as simple staining procedures and complex analytical methods have been used for sorghum nutrient profiling. Staining techniques provide a crude estimation of micro-nutrients, while analytical methods such as X-ray fluorescence spectrometer (XRF) are robust and preferred for micronutrient analysis (Kumar et al., 2019). The XRF technology has proven to be an accurate and more rapid method to determine the nutrient profiles of cereal grain than the wet chemistry procedures (Guild and Stangoulis, 2021). The XRF technology can simultaneously determine multiple nutrient contents at low costs in a large sample size.
Previous studies have reported the presence of significant genetic variation among sorghum genetic resources from East Africa for agronomic and quality traits (Akatwijuka et al., 2016;Kataka et al., 2018;Kiprotich et al., 2015;Olweny et al., 2015;Salih, 2011). However, these studies either used limited accessions, focused on yield-related agronomic traits, and neglected nutritional quality traits. The genetic diversity of East African sorghum collection should be assessed with nutritional quality and farmer-preferred traits using the representative number of germplasm. No study was conducted using a higher number of East African sorghum germplasm to unravel the genetic diversity while targeting Fe and Zn content and agronomic traits.
In Uganda, sorghum quality breeding has received little research and development support compared with the major staple crops. The genetic variation for quality traits in Ugandan sorghum germplasm and the existing diverse sorghum germplasm of East African collections is yet to be explored using agronomic and nutritional quality traits for breeding. Therefore, the objective of this study was to determine the genetic diversity present among East African sorghum germplasm collections for agronomic and nutritional quality traits to select promising lines for direct production or breeding. Information presented in the study serves as baseline data for nutritional quality improvement in the country or similar agro-ecologies in East Africa.

Germplasm source and experimental design
The study used 348 sorghum genotypes collected from East Africa. Twenty-six genotypes were acquired from the National Semi-Arid Resources Research Institute (NaSARRI)/Uganda, while four check varieties released in Uganda were included. The remaining germplasm were introductions from ICRISAT/Kenya as follows: 318 landrace collections from East Africa, including from Kenya (232), Tanzania (49), and South Sudan (37). However, three genotypes (1 from South Sudan and 2 from Kenya) were excluded due to poor germination. As described below, the Core ideas Conventional plant breeding enhances sorghum nutrient concentrations. There is adequate genetic diversity among East African sorghum germplasm for breeding. In the assessed sorghum populations, grain Fe and Zn contents positively correlated but negatively correlated with grain yield. Grain yield and Zn content had high heritability and genetic advance. Six genotypes had high grain Fe and Zn contents among the assessed sorghum germplasm for production or genetic improvement. nutritional quality traits (i.e. Fe and Zn) were assayed from 334 sorghum genotypes. The description of the germplasm used in the study is presented in Table 1. The germplasm collections were field evaluated using a 22 Â 20 augmented block design across two locations in Uganda. The experimental unit consisted of a 2.4 m width by 2 m long plot with sorghum genotypes planted using an inter-row spacing of 0.6 m and intra-row spacing of 0.2 m. The experimental units at all locations were maintained following the standard agronomic practices of sorghum in Uganda (Lubadde et al., 2019).

Study locations
The study was conducted at two sites in Uganda, including the National Semi-Arid Resources Research Institute (1 35 0 N 33 35 0 E) and Abi Zonal Agricultural Research Development Institute (AbiZARDI) (3 4.58 0 N 30 56 0 E) during the summer growing seasons of 2019 and 2020. The study locations represent the major sorghum production agroecologies in Uganda. NaSARRI is located in the east of Uganda at an altitude of 1140 m above sea level (m.a.s.l) and receives total annual rainfall ranging between 900 to 1000 mm with a bimodal distribution and has a mean annual temperature of 26 C. AbiZARDI lies in the northwest of Uganda at an altitude of 1,215 m.a.s.l, receives an average rainfall of 1,404 mm per year, has a unimodal rainfall pattern, and a mean annual temperature of 23.9 C. Both sites are characterized by sandy loam soils (Salvaradjou et al., 2005).

Data collection
Data were collected on 20 agro-morphological traits, including seven qualitative and 13 quantitative traits following the descriptors for sorghum (IBPGR, 1993), which are summarised in Table 2. Five selected and tagged panicles per genotype were covered with a brown bag before Table 1. The 334 accessions used in the study with their source and resolved genetic clusters when assessed using 11 quantitative traits across two locations in Uganda. flowering to minimize cross-pollination and harvest true-to-type seeds for grain Fe and Zn analysis. Harvesting was done manually at physiological maturity at all the study sites. After harvesting, panicles were sun-dried to obtain a seed moisture content of 12.5%. Panicles from each plot were later threshed, winnowed, and seeds packed. A composite seed sample of 100g from each accession was weighed, tagged, and packed in clean cloth bags and sent to ICRISAT/Niamey in Niger for grain micronutrient analysis and to profile the grain Fe and Zn content by an X-ray fluorescence spectrometer (XRF) method.

Data analysis
Data collected on qualitative traits were subjected to analyses, including frequency distribution, cross-tabulation, and Shannon-Weaver diversity indices. The statistical package for social scientists (SPSS) version 25.0 (George and Mallery, 2019) and Microsoft Excel were used to capture and analyze data. The qualitative phenotypic diversity among sorghum accessions was estimated using Shannon-Weaver diversity indices (H 0 ) based on the frequency data as described by Jain et al. (1975). The diversity index H 0 and Shannon's equitability, E H ,was calculated as: H ¼ ΣP i log e P i , where: H ¼ Shannon diversity index, P i ¼ proportion of accessions in the i th class of an n class trait in a population.
E H ¼ H Hmax , where: H max ¼ lnS, S ¼ total number of species in the community (richness).
Data collected on quantitative traits were subjected to statistical analyses using the descriptive statistics for each experimental site separately using the R package for augmented design (Aravind et al., 2021). Shoot fly count and stem borer count data were normalized by square root transformation before analysis of variance. A combined analysis of variance was conducted using pooled data of adjusted mean values across the experimental sites using the R software version 4.1.0 (R Core, 2021).
The phenotypic, genotypic, and environmental variances denoted as σ 2 p,  σ 2 g, and σ 2 e, respectively, were computed from the expected mean square values as described by Federer and Searle (1976). The phenotypic, genotypic, and environmental coefficients of variation denoted as PCV, GCV, and ECV, respectively, were calculated according to Muchira et al. (2021). The GCV and PCV estimates were classified according to Sivasubramanian and Madhavamenon (1973) scales, where values of 0-10% represented low, 11-20% moderate, and >20% high. Broad-sense heritability (H 2 ) values were estimated according to Lush (1940), and values were classified according to Robinson (1966), where H 2 values of 0-30% represented low, 31-60% medium, and >61% high. The expected (predicted) genetic advance was calculated as the product of broad-sense heritability and phenotypic standard deviation at a selection intensity of 5% according to Johnson et al. (1955) as follows: GA ¼ H 2 x σ p x k, where: GA ¼ expected genetic advance; k ¼ selection differential in standardized units or a selection intensity of 5% ¼ 2.056; and σ p ¼ phenotypic standard deviation. The genetic advance was expressed as a percent of the mean of the unselected parental population, GA% ¼ GA x x 100, where: GA% ¼ Genetic advance as percent of the mean, x ¼ Population mean for the trait considered. The GA% values were classified following Johnson et al. (1955) as follows: values of 0-10% denoted low, 11-20% moderate, and >20% high.
Based on the pooled data, principal component, correlation, and cluster analyses were conducted with the R software version 4.1.0. Principal component analysis was computed to determine the major components that could group the agro-morphological traits and examine each trait's percentage contribution to total genetic variation. Correlation coefficients among the studied traits were performed using the matrix procedure CORR and the optional PEARSON method in R software. Phenotypic cluster analysis was done using the unweighted pair group method with arithmetic mean (UPGMA) based on Euclidean distance matrix (Mayor et al., 2004;Spark, 1973) to obtain a K-means cluster.

Variation for qualitative traits
The test genotypes showed marked genetic variations for qualitative traits across the study sites (Table 3). Significant genotype variation (p < 0.001) was recorded for all the assessed qualitative traits such as glume color, leaf midrib color, grain color, inflorescence shape and compactness, glume covering, and presence of awn on the panicle (Table 3). The white leaf midrib-color (displayed by 70.4% of the assessed genotypes) and naked or awnless panicle at maturity (92.5%) were the most predominant traits in the assessed sorghum accessions. Sixty percent of the genotypes had 25% of their grain covered by glumes, followed by a glume coverage of 50% (32.2%) and 75% (5.5%). The predominant inflorescence shape was of drooping type (44.9%), followed by the elliptic (42.0%), oval (11.3%), and erect (1.7%) types. About 48.4% of accessions had a loose inflorescence, followed by a semi-compact (30.7%) type. Only 20.6% of the accessions had compact inflorescence, while 0.3% had semi-loose inflorescence. The collections comprised red glume (75.7%) and yellow glume (18.2%) types. Diverse grain colors were observed in the germplasm, with the most prevalent brown seed color at 35.4%. Other grain colors present in the germplasm were red (27.3%), yellow (18.8%), white (16.5%), and buff (a light brown to the yellow group) (2.0%). Most sorghum genotypes (50.5%) had exerted inflorescence (2-10 cm between ligule and inflorescence base) followed by well-exserted inflorescence; >10 cm between ligule and inflorescence base (35.9%), peduncle recurved inflorescence exsertion, i.e., inflorescence below ligule and clearly exposed splitting the leaf sheath (8.2%), and slightly exserted inflorescence exsertion, i.e., <2 cm but ligule of flag leaf below inflorescence base panicle exsertion length (5.5%). A large collection of the genotypes (62.4%) had a good agronomic score, while The analysis indicated that there were significant differences (p < 0.05) among all the genotypes, including the test populations, and check varieties for all the assessed agronomic traits except for 100 seed weight at both locations (Table 4). Test genotypes showed variation for stem borer count and 100 seed weight at the NaSARRI and AbiZARDI sites, respectively. A significant difference (P 0.05) was also recorded for the test vs. check interaction for most of the traits except for shoot fly count, stem borer count, rachis number, grain filling duration, dry panicle Table 3. Shannon diversity indices, corresponding proportion (%), and significance tests for qualitative traits among 345 sorghum genotypes evaluated in two locations in Uganda.
weight, Fe and Zn content (Table 4). A combined analysis of variance was conducted using pooled data across the experimental locations. The genotype Â environment interaction effects were significant (p < 0.05) for plant height, panicle length, panicle width, rachis number, days to 50% flowering, grain filling duration, days to 75% maturity, and Zn content ( Table 4). The genotype main effect was significant (p < 0.01) for all assessed traits except stem borer count, while the location effect was nonsignificant (p < 0.05) for plant height and rachis number.

Performance of assessed sorghum genotypes for grain yield and quality traits across locations
Based on grain yield performance, 32 genotypes had !12% yield gain over the best commercial check cultivar (NAROSORG3), while 80 genotypes had grain yields higher than the best commercial check cultivar (NAROSORG3) ( Table 5). A mean yield of 2909.3 kg ha À1 was attained across the study locations. Genotype GBK 000955 had the highest grain yield of 4899.9 kg ha À1 , which was 31.9% higher than the best commercial check cultivar (NAROSORG3). The next best-ranked genotype was GE/30/1/2013A with grain yield gain of 31.2% followed by genotypes SILA (29.6% yield gain), GBK 034699 (28.6%), GBK 044111 (28.2%), GBK 043040 (27.2%), GBK-051589 (26.3%), GBK 000445 (25.9%), GBK-051521 (25.1%), and Epuripur (24.6%). Most of the high yielding genotypes were early to medium maturing with comparatively short to medium plant height compared with the commercial check cultivars such as SESO1 and SESO3. All the 32 best performing genotypes, including the checks, had grain Fe and Zn content below the standard acceptable levels of Fe (>60 ppm) and Zn (>32ppm). Five genotypes recorded higher grain Fe concentrations. These were: Tanzania Acc#42 (with a mean Fe and Zn content of 65.5 ppm and 10.2 ppm, respectively), Tanzania Acc#8 (64.7 ppm Fe and 26.3 ppm Zn), IS 3790 (63.5 ppm Fe and 24.6 ppm Zn), IS 30310 (63.3 ppm Fe and 26.4 ppm Zn), and SUDAN COLL# 7 (61.3 ppm Fe and 25.0 ppm Zn). Genotype IS 12750 recorded a higher grain Zn concentration with a mean Zn content of 32.5 ppm ( Table 6). The standard acceptable level of Fe is > 60 ppm and Zn > 32ppm for crop biofortification programs (Chapke and Tonapi, 2016). The six genotypes had relatively low grain yield levels compared to the commercial check cultivar except Tanzania Acc#42 (3315.3 kg ha À1 ) and IS 30310 (3534.2 kg ha À1 ), which had medium plant height and early to medium maturity ( Table 6).
The six high grain Fe and Zn dense sorghum genotypes had a moderately high stem borer count (24.2-54.6%) and shoot fly count (2.8-13.4%) compared to the commercial check cultivar, SESO1 at 15.0% and 2.6%, respectively.

Genetic parameters for 13 quantitative traits in 345 sorghum genotypes evaluated in two locations in Uganda
The magnitude of GCV estimates was lower than the corresponding PCV estimates for the quantitative traits studied across locations (Table 7). High PCV value was recorded for shoot fly count (42.2%), grain yield (32.7%), panicle length (28.3%), 100 seed weight (27.7%), stem borer count (27.4%), plant height (26.4%), rachis number (26.3%), grain Zn (23.6%), and grain Fe (22.2%). Plant height, panicle length, panicle width, rachis number, days to 50% flowering, grain filling duration, and days to 75% maturity had relatively the same PCV and GCV values (Table 7). Some traits such as shoot fly count, stem borer count, and grain yield differed in their PCV and GCV estimates. High ECV estimates were recorded for shoot fly count and stem borer count.

Principal component and biplot analyses
The principal component analysis (PCA) results are summarised in Table 8. Six principal components (PCs) with eigenvalues greater than one accounted for 75% of the total variability. The first principal component (PC1) explained a variation of 29.0% followed by PC2 (13.0%) and PC3 (11.0%). The traits with a significant contribution in PC1 were days to 75% maturity, days to 50% flowering, plant height, grain yield, and 100 seed weight. While in PC2, most of the variation was contributed by panicle width, grain filling duration, and stay green. The highest positive contribution to the variation accounted for in PC3 was from panicle exsertion, panicle length, rachis number, and grain filling duration. High contribution to the variation in PC4 was attributed by grain Fe and Zn contents but in a negative dimension. The principal component biplots delineated the accessions into four groups according to the sources of germplasm collection (Figure 1). Accessions from Uganda were distinct from Sudan. The accessions from the furthest right side of PC1 were collections mainly from Sudan and Tanzania and grouped according to plant height, days to 50% flowering, days to 75% maturity days, Fe and Zn contents, panicle length, rachis number, and panicle exsertion.
Ugandan accessions were grouped based on a higher 100 seed weight on the furthest left side of PC1 (Figure 1). ICRISAT accessions with high grain Fe, panicle length, rachis number, grain Zn, panicle exsertion, and grain filling duration were clustered in PC1. There was no definite delineation pattern between the accessions along the first two PC axes due to the overlap among the accessions in the biplot. The assessed quantitative traits were categorized according to the size of angles between dimension vectors (Figure 1). For example, plant height, days to 50% flowering, and days to 75% maturity had smaller angles between dimension vectors, thus high correlation among these variables. There was a moderate positive association between grain yield and 100 seed weight.
Cluster III had the least number of genotypes (53) characterized by moderate to high grain yield (with a mean of 3247.04 kg ha À1 ), medium plant height (245.2 cm) and short days-to-75% maturity (116.9 days), and moderate Fe concentration (38.0 ppm) and short grain-filling duration (38.1 days). Cluster IV had 87 genotypes with high grain yield (4108.3 kg ha 1 ), short plant height (229.4 cm), early maturity (113.0  days), low Fe (34.8 ppm) and Zn (17.2 ppm) concentration, low rachis number (47.6) with wide panicle width (6.4 cm) compared to genotypes in other clusters.

Correlation between yield and yield components and grain iron and zinc concentrations
The pair-wise correlation coefficients between the assessed traits of the 334 sorghum genotypes evaluated in two locations in Uganda is presented in Table 10. Grain yield had a moderate positive and significant association (P < 0.001) with 100 seed weight (r ¼ 0.43) and panicle width (r ¼ 0.37). Grain yield showed negative and significant association (P < 0.001) with days to 75% maturity (r ¼ -0.43), days to 50% flowering (r ¼ -0.36), and plant height (r ¼ -0.31). There was a relatively low but significantly negative association between grain yield and Fe (r ¼ -0.26) and Zn (r ¼ -0.17) contents. A significant positive correlation was recorded between grain Fe and Zn contents (r ¼ 0.32). There was a weak association between grain Fe/Zn concentration and other key sorghum yield components such as days-to-50% flowering, plant height, days-to-75% maturity, panicle length, panicle width, and 100 seed weight.

Discussion
Sorghum is one of the principal cereal crops for food security in Africa and Asia and a source of raw material for the global beverage and syrup industry. Sorghum has multiple health benefits with products that are gluten-free and rich in phenolic compounds acting as antioxidants. Nutritionally enhanced sorghum is vital to reduce malnutrition among the rural and urban poor communities who depend on sorghum as their food staple. However, sorghum nutritional quality breeding has received little research and development support compared with other major staple crops such as maize and wheat. The genetic variation present in East African sorghum germplasm can be explored for quality traits and develop new varieties with farmer and market preferred product profiles. Therefore, this study was initiated to provide a comprehensive genetic diversity analysis to unravel the agronomic performance and nutritional quality traits among East African sorghum germplasm collections.
The present study found high diversity index of 0.92 for quality traits among the assessed 345 genotypes (Table 3). The magnitude of the diversity is higher than the mean diversity index reported by Desmae et al. (2016) at 0.67 in 974 landrace sorghum populations sampled from North-Eastern Ethiopia. The recorded genetic variability for quality traits was pronounced for grain color, stay green, panicle exsertion, and inflorescence type. Harlan and de Wet (1972) reported extensive variability in sorghum grain color, panicle exsertion, and inflorescence type. Farmers and breeders widely use these quality traits for selection in the region. Most germplasm collections (62.6%) assessed in this study had red and brown grain colors with loose inflorescence and drooping shapes. Sawadogo et al. (2014) reported that most (78.40%) of sorghum genotypes assessed in Burkina Faso had loose panicle shapes consistent with the current study. The predominantly red and brown grain color observed in >62% of the assessed sorghum genotypes indicates farmer preferences and long-term selection towards these traits. In a recent participatory rural appraisal study by Andiku et al. (2021), brown and light red grain sorghum cultivars were the most preferred traits by farmers. Sorghum flour prepared from brown or red grain blends well with cassava flour to prepare porridge locally referred to as ugali. White grain sorghum is mainly used for brewing. However, white grain types of sorghum are less preferred by farmers due to bird damage in the field and susceptibility to grain mold.
(92.5%) of the tested genotypes were awnless at maturity, while 60% had their grains covered by red glume at 25%. This finding concurs with the studies of Sawadogo et al. (2014) in BurkinaFaso, Nadjiam (2021) in Chad, and Desmae et al. (2016) in Ethiopia, who reported that the majority of their germplasm collections had 25% glume cover. High glume cover is associated with poor threshing ability and reduced seed size (Desmae et al., 2016). The current study found glume color variation that can be selected and explored for grain mold resistance breeding. Sorghum genotypes with dark grain glumes are reportedly mold-resistant (Das et al., 2020). In the present study, a low level of genetic variation was recorded for leaf midrib (Table 3). White leaf midrib was present in 70.4% of the tested genotypes. This is contrary to Verma et al. (2017), who reported a high level of genetic variation for leaf midrib color among sorghum collections in India.
The test genotypes showed significant (P 0.05) variation for grain yield, nutritional quality traits, and quantitative agronomic traits (Table 4). This suggests that the germplasm pool harbor adequate genetic variation for breeding nutritionally enhanced and high-yielding varieties. Previous studies by Akatwijuka et al. (2016), Desmae et al. (2016), Kumar et al. (2012), Hariprasanna et al. (2014), Ng'uni et al. (2012 disclosed sufficient genetic variation for the quantitative traits in sorghum accessions sampled from Uganda, Ethiopia, India, and southern Africa. The high degree of genetic variation is attributed to the differences in their genetic constitution and the long selection history of the materials in various geographical locations in East Africa, including Kenya, Sudan, Tanzania, and Uganda. Eighty genotypes expressed relatively higher grain yield (12.0-31.9%) compared with the best commercial check cultivar (NAROSORG3) ( Table 5). These genotypes had early to medium maturity, short to medium plant height, and low grain Fe and Zn accumulation. Conversely, six genotypes (Tanzania Acc#42, Tanzania Acc#8, IS 3790, IS 30310, SUDAN COLL# 7, and IS 12750) were selected with relatively higher Fe and Zn concentrations ( Table 6). The high levels of Fe and Zn contents in the present study agree with previous findings, including Kumar et al. (2013a), Hariprasanna et al. (2014), Ng'uni et al. (2012, and Reddy et al. (2010). Hence, these accessions are ideal candidates for Fe and Zn enhancement as donor parents. The grain yield of these genotypes was markedly low. Nevertheless, they can be used to introgress the nutritional quality genes into genotypes with high yield potential. The six Zn and Fe dense genotypes recorded moderately high stem borer and shoot fly damage indicating their susceptibility to insect pests (Table 6). Therefore, during nutritional quality breeding, multiple trait selection strategies should be pursued to enhance the genetic gains for yield and yield components.
The magnitude of PCV, GCV, heritability, and genetic advance is directly related to selection response. The extent of GCV estimates was lower than the corresponding PCV for all the quantitative traits (Table 7). The lower GCV than PCV estimates suggest a strong environmental influence on the expression of the phenotypic traits. Also, the PCV estimates for Fe and Zn concentrations were higher than their corresponding GCV. The high influence of the environmental variance suggests the need for genotype by environment interaction (GEI) analysis during nutritional quality trait improvement to select stable performing genotypes. Phuke et al. (2017) proposed that GEI assessment is key in selecting sorghum genotypes with stable Fe and Zn expression. The magnitudes of PCV and GCV values for plant height, panicle length, rachis number, days  to 50% flowering, grain filling duration, and days to 75% maturity were relatively close, indicating the low environmental effects and allowing for direct selection for these traits (Semahegn et al., 2021). High H 2 (!60%) and GA% (23.2-56.2%) were recorded for grain yield and plant height, panicle length, panicle width, rachis number, days to 50% flowering, grain filling duration, days to 75% maturity, and 100 seed weight (Table 7). The high values for these traits offer a higher selection response. Gebregergs and Mekbib (2020) and Gebreyohannes et al. (2018) recorded high H 2 estimates and GA% for grain yield, plant height, 100 seed weight, and panicle length among sorghum collections of Ethiopia. Also, high H 2 (63.7%) and GA% (31.0%) were recorded for Zn concentration, indicating that this trait is probably under the influence of an additive gene effect, and presumably, its improvement could be achieved through targeted recurrent selection. Kumar et al. (2015) reported that Fe and Zn concentrations are highly heritable, under additive genetic control, and can be selected with a high grain yield. Other studies reported dominant gene action affecting Fe concentration in sorghum (Hariprasanna et al., 2014;Kumar et al., 2013b). Slightly low H 2 (53.1%) and GA% (24.4%) were recorded for Fe concentration in the current study.
The PCA discerned important traits contributing to the largest reliable variability among genotypes. In this study, days to 75% maturity, days to 50% flowering, grain yield, 100 seed weight, plant height, panicle width, grain filling duration, and stay green were the most significant traits and accounted for the largest variation allocated in the first two PCs loadings (Table 8 and Figure 1). These traits have much influence on selection during crop improvement. Naoura et al. (2019), Mangena et al. (2018), and Abraha et al. (2015) reported that maturity days, days to 50% flowering, grain yield, 100 seed weight, plant height, panicle length, and stay green were highly influential traits and contributed the most to the total genetic variation in sorghum landraces. The PCA biplot classified the genotypes into four groups (Figure 1) based on the relationship between principal components, the phenotypic traits, and the genotypes. The assessed quantitative traits were categorized according to the size of  angles between dimension vectors proposing a strong association among the traits (Mangena et al., 2018). For example, in the current study, a strong association was observed between plant height, days to 50% flowering, and days to 75% maturity; and grain yield and 100 seed weight. Accessions from Sudan and Tanzania were grouped on the right side of PC1 according to plant height, days to 50% flowering, days to 75% maturity days, grain Fe and Zn, panicle length, rachis number, and panicle exsertion ( Figure 1). Conversely, Ugandan accessions were grouped based on a higher 100 seed weight on the left side of PC1. ICRISAT accessions were clustered in PC1 based on grain Fe and Zn, panicle length, rachis number, panicle exsertion, and grain filling duration.
The cluster analysis delineated the genotypes into four distinct categories (Table 9 and Figure 2), suggesting the presence of substantial genetic variation among the tested genotypes. Genotypes with similar agronomic performance were grouped irrespective of their sources of collection. For example, genotypes with low grain yield, high rachis number, tall plant height, late maturity, and high grain Fe concentration with moderate Zn concentration were grouped in Cluster I regardless of their origin (Table 9 and Figure 2). On the other hand, high yielding genotypes with short plant height, early maturity, and low Fe and Zn concentrations were allocated in Cluster IV. Genotypes with high Zn concentration and tall plant height with medium to late flowering days were assigned to Cluster II. This pattern of distribution could be related to  Table 1 for the codes of genotypes and Table 9 for phenotypic traits. PHT -0.40*** 0.12*** 0.08ns 0.68*** 0.05ns 0.70*** 0.26*** 0.18*** -0.29*** -0.31*** PNL --0.20*** 0.13* 0.36*** 0.30*** 0.46*** 0.25*** 0.23*** -0.10ns -0.20*** PNW --0.08ns 0.07ns -0.12* 0.04ns -0.04ns -0.14* 0.07ns 0.37*** duration (days); DTM ¼ Days to 75% maturity (days); Fe ¼ Iron content (ppm); Zn ¼ Zinc content (ppm); HSW ¼ 100 seed weight (g); YLD ¼ Grain yield (kg/ha); ns ¼ P > 0.05; * ¼ P 0.05; ** ¼ P 0.01; *** ¼ P 0.001. seed exchange among farmers, research organizations, and nongovernmental organizations in addition to cross-cutting agro-ecologies, cultures, and end uses in eastern Africa. Suitable parent selection with the trait of interest for crop improvement could be accomplished by integrating these records. In the present study, a significant (P < 0.01) association was exhibited between grain yield and all the quantitative traits (Table 10). Grain yield had a moderate positive and significant association with 100 seed weight and panicle width. This implies that these traits can be improved concurrently through direct selection. However, there was a significant negative association between grain yield and Fe and Zn concentrations. Reddy et al. (2005) reported a significant negative association between micronutrients (i.e., Fe, and Zn) and grain yield in sorghum. A strong positive association was recorded between grain Fe and Zn contents. (2010) reported a positive and significant association between grain Fe and Zn concentrations. The strong correlations between these two micronutrients suggest the possibility of concurrent improvement in these traits (Bhat et al., 2018;Kumar et al., 2013a). In the present study, days to 50% flowering, plant height, days to 75% maturity, and 100 seed weight had a weak association with grain Fe and Zn concentrations. This suggested that enhancing nutritional quality traits (i.e., Fe, and Zn) with farmer desired traits can be attained without compromising grain yield in sorghum. Interestingly, notable positive associations were recorded between agronomic traits that would allow simultaneous selection of these traits.

Conclusions
The current study found a high magnitude of genetic variations for grain yield and related traits, grain Fe and Zn concentrations among East African sorghum germplasm collections. Six genotypes (Tanzania Acc#42, Tanzania Acc#8, IS 3790, IS 30310, SUDAN COLL# 7and (IS 12750) with high grain Fe and Zn concentrations were identified as breeding parents for nutritional quality enhancement. Plant height, panicle length, panicle width, rachis number, days to 50% flowering, grain filling duration, days to 75% maturity, grain yield, 100 seed weight, and grain Zn concentration had relatively high heritability and genetic advance as percent of the mean. The present findings suggest that the identified traits are amenable to improvement through selection. The assessed sorghum germplasm was differentiated into four distinct genetic groups based on the cluster analysis. The study further found a significant positive association between grain Fe and grain Zn concentration (r ¼ 0.32, P < 0.001) to pursue concurrent breeding for enhanced grain yield and the two nutritional traits in sorghum.

Author contribution statement
Charles Andiku: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.
Hussein Shimelis: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.
Admire I.T. Shayanowako: Analyzed and interpreted the data; Wrote the paper. Prakash I. Gangashetty; Eric Manyasa: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Funding statement
This study was supported by Bill and Melinda Gates Foundation [OPP1129015 and INV-009649_2020].

Data availability statement
Data will be made available on request.